Spaces:
Runtime error
Runtime error
File size: 5,279 Bytes
563baab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
import pandas as pd
from .preprocess import get_dataset_from_csv
from huggingface_hub import from_pretrained_keras
##Load Model
model = from_pretrained_keras("shivi/classification-grn-vsn")
def batch_predict(input_data):
"""
This function is used for fetching predictions corresponding to input_dataframe.
It outputs another dataframe containing:
1. prediction probability for each class
2. actual expected outcome for each entry in the input dataframe
"""
input_data_file = "prod_data.csv"
labels = ['Probability of Income greater than 50000',"Probability of Income less than 50000","Actual Income"]
predictions_df = pd.DataFrame(columns=labels)
input_data.to_csv(input_data_file, index=None, header=None)
prod_dataset = get_dataset_from_csv(input_data_file, shuffle=True)
pred = model.predict(prod_dataset)
for prediction, actual_gt in zip(pred, input_data['income_level'].values.tolist()):
y_pred_prob = round(prediction.flatten()[0] * 100, 2)
y_not_prob = round((1-prediction.flatten()[0]) * 100, 2)
y_pred = ">50000" if prediction.flatten()[0] > 0.5 else "<50000"
prob_scores = {labels[0]: str(y_pred_prob)+"%" , labels[1]: str(y_not_prob)+"%", labels[2]: y_pred}
predictions_df = predictions_df.append(prob_scores,ignore_index=True)
return predictions_df
def user_input_predict(age, wage, cap_gains, cap_losses, dividends, num_persons, weeks_worked_in_year,
class_of_worker, detailed_industry_recode,detailed_occupation_recode,education,
enroll_in_edu_inst_last_wk, marital_stat, major_industry_code,major_occupation_code,
race, hispanic_origin, sex, member_of_a_labor_union,reason_for_unemployment,
full_or_part_time_employment_stat, tax_filer_stat,region_of_previous_residence,
state_of_previous_residence,detailed_household_and_family_stat,detailed_household_summary_in_household,
migration_codechange_in_msa,migration_codechange_in_reg, migration_codemove_within_reg,
live_in_this_house_1_year_ago,migration_prev_res_in_sunbelt,family_members_under_18,
country_of_birth_father,country_of_birth_mother,country_of_birth_self,
citizenship,own_business_or_self_employed,fill_inc_questionnaire_for_veterans_admin,
veterans_benefits, year):
"""
This function is used for fetching model predictions based on inputs given by user on demo app
"""
input_dict = {"age": [age],
"class_of_worker": [class_of_worker],
"detailed_industry_recode": [detailed_industry_recode],
"detailed_occupation_recode": [detailed_occupation_recode],
"education":[education],
"wage_per_hour": [wage],
"enroll_in_edu_inst_last_wk": [enroll_in_edu_inst_last_wk],
"marital_stat": [marital_stat],
"major_industry_code": [major_industry_code],
"major_occupation_code": [major_occupation_code],
"race": [race],
"hispanic_origin": [hispanic_origin],
"sex": [sex],
"member_of_a_labor_union": [member_of_a_labor_union],
"reason_for_unemployment": [reason_for_unemployment],
"full_or_part_time_employment_stat": [full_or_part_time_employment_stat],
"capital_gains": [cap_gains],
"capital_losses": [cap_losses],
"dividends_from_stocks": [dividends],
"tax_filer_stat": [tax_filer_stat],
"region_of_previous_residence": [region_of_previous_residence],
"state_of_previous_residence": [state_of_previous_residence],
"detailed_household_and_family_stat": [detailed_household_and_family_stat],
"detailed_household_summary_in_household": [detailed_household_summary_in_household],
"instance_weight": [0.0],
"migration_code-change_in_msa": [migration_codechange_in_msa],
"migration_code-change_in_reg": [migration_codechange_in_reg],
"migration_code-move_within_reg": [migration_codemove_within_reg],
"live_in_this_house_1_year_ago": [live_in_this_house_1_year_ago],
"migration_prev_res_in_sunbelt": [migration_prev_res_in_sunbelt],
"num_persons_worked_for_employer": [num_persons],
"family_members_under_18": [family_members_under_18],
"country_of_birth_father": [country_of_birth_father],
"country_of_birth_mother": [country_of_birth_mother],
"country_of_birth_self": [country_of_birth_self],
"citizenship": [citizenship],
"own_business_or_self_employed": [own_business_or_self_employed],
"fill_inc_questionnaire_for_veterans_admin": [fill_inc_questionnaire_for_veterans_admin],
"veterans_benefits": [veterans_benefits],
"weeks_worked_in_year": [weeks_worked_in_year],
"year": [year],
"income_level": [0],
}
input_df = pd.DataFrame.from_dict(input_dict)
input_data_file = "input_data.csv"
input_df.to_csv(input_data_file, index=None, header=None)
prod_dataset = get_dataset_from_csv(input_data_file, shuffle=True)
labels = ['Income greater than 50000',"Income less than 50000"]
prediction = model.predict(prod_dataset)
y_pred_prob = round(prediction[0].flatten()[0],5)
y_not_prob = round(1-prediction[0].flatten()[0],3)
confidences = {labels[0]: float(y_pred_prob), labels[1]: float(y_not_prob)}
return confidences
|